Evaluation of perceived and actual competency in a family medicine objective structured clinical examination.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between objective assessment of performance and self-rated competence immediately before and after participation in a required summative family medicine clerkship objective structured clinical examination (OSCE). DESIGN: Learners rated their competence (on a 7-point Likert scale) before and after an OSCE along 3 dimensions: general, specific, and professional competencies relevant to family medicine. SETTING: McGill University in Montreal, Que. PARTICIPANTS: All 168 third-year clinical clerks completing their mandatory family medicine rotation in 2010 to 2011 were invited to participate. MAIN OUTCOME MEASURES: Self-ratings of competence and objective performance scores were compared, and were examined to determine if OSCEs could be a "corrective" tool for self-rating perceived competence (ie, if the experience of undergoing an assessment might assist learners in recalibrating their understanding of their own performance). RESULTS: < .001 for all). CONCLUSION: After the OSCE, students' self-ratings of perceived competence had decreased, and these ratings had little relationship to actual performance, regardless of the specificity of the rated competency. Discordance between perceived and actual competence is neither novel nor unique to family medicine. However, this discordance is an important consideration for the development of competency-based curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".